"Unfortunately the reality of LLM-based contributions has been mostly negative for us, from an increase in background noise due to worthless drive-by PRs full of hallucinations (that wouldn’t even compile, let alone pass CI), to insane 10 thousand line long first time PRs. In-between we also received plenty of PRs that looked fine on the surface, some of which explicitly claimed to not have made use of LLMs, but where follow-up discussions immediately made it clear that the author was sneakily consulting an LLM and regurgitating its mistake-filled replies to us."
I don't think this is realistic. I'm a good programmer, and it speeds up my work a lot, from "make sense of this 10 repo project I haven't worked on recently" to "for this next step I need a vpn multiplexer written in a language I don't use" to, yeah, "this 10k line patch lets me see parts of design space we never could have explored before." I think it's all about understanding the blast radius. Sonetimes a lot of code is helpful, sometimes more like a lot of help proving a fact about one line of code.
Like Simon says, if I'm driving by someone else's project, I don't send the generated pull request, I just file the bug report / repro that would generate it.
but that acceleration is exactly because you're not good at that language
And how do you know if that is the case or the person/team using the LLMs is one of the good ones?
So the safest answer is just "no".
Speed is seductive.
The bar isn't "this is a known good contributor". Its "this is a known good contributor working in a space they have knowledge in and has a track record of actually checking and thinking about LLM output before submitting it." It's much higher and I don't see how you can approve people on an organization-wide basis.
> LLMs make me significantly faster at writing code I was mediocre or bad at. But when I use it to write code in domains I have more knowledge in I see design and correctness problems all over the place and actively fix them and it slows down my output.
i am reminded of this quote: it takes more cleverness to debug code than it takes to write it. if you write code as clever as you can, by definition you are not clever enough to debug it. using LLM makes your code many times more clever than what you could write yourself. which means by the same definition the code is to clever for you to understand or debug it.
The entire problem is that before the meatbag code was either not submitted at all (developer knowing they are not competent enough to do the fix) or the volume of it was low.
With LLM people not competent to even review, let alone write are emboldened to just throw shit on the wall at rapid pace. So the wall is entirely covered by the shit
The problem with this line of thinking is the same with "I so good as C developer, my code is so-safe!".
And we see what reality instead tell: Yes, exist people where this claims are true, not, is not even a decently sized minority.
i know what a kernel module is and im reasonably certain that the patch is safe, but there is no way in hell i would have found that solution (i would have given up). in a world without llms, the project would have died.
Time and time again I've had a project (such as a DSL to SQL compiler, automatic Rust codegen, CSS development) stall because the LLM took a short sighted decision.
I later found better solutions by querying Reddit and upon consulting the LLM, it basically said "oh shit I'm sorry"
It's honestly pretty arrogant to tell a senior engineer that you "really hope" they've gone over some code. AI generated or otherwise.
I really hope usted checked your code
At this point I'm pretty sure I did the homework for people in college who are now senior engineers
This is commonplace. So commonplace that most have worked “checking the LLM” into their workflow so deeply that essentially all that’s done is prompt followed by a mini code review.
To suggest a senior engineer blindly accepts modifications without code review kinda hints at you not using LLMs to realize how quickly it will make a mess of things if you don’t hold it’s hand.
we have 2 very high value DAU, one of whom is me, and probably will max out at 1000 in our wildest dreams.
long term, our biggest concern is a security regression that lets outsiders see our internal information
Whenever I see this arguement, I'm reminded that most programmers don't know what they do for work
You can't review it.
Are you relying on your colleagues to do that, or is this riddled with bugs? Or is it code you're producing for personal use only so it's not worth mentioning as it's not sped your work up, it's just let you write a little play program.
Why are they often so desperate to lie and non-consensually harass others with their vibing rather than be honest about it? Why do they think they are "helping" with hallucinated rubbish that can't even build?
I use LLMs. It is not difficult to: ethically disclose your use, double check all of your work, ensure things compile without errors, not lie to others, not ask it to generate ten paragraphs of rubbish when the answer is one sentence, and respect the project's guidelines. But for so many people this seems like an impossible task.
Because they can't tell the difference between what the machine is outputting, and what people have built. All they see is the superficial resemblance (long lines of incomprehensbile code) and the reward that the people writing the code have got, and want that reward too.
AI is absolutely terrible for people like that, as it's the perfect enabler.
It's not about helping. It's about the feeling of clout. There are still plenty of people who look at Github profile activity to judge job candiates, etc. What gets measured gets repeated.
I believe that most of the ills of social media would disappear, if we eliminated the "like" and "upvotes" buttons and the view counts. Most open source garbage pull requests may likewise go away if contributions were somehow anonymous.
This might not be helped by the fact that there are a lot of seemingly psychotic commenters attacking anything which might have touched an LLM or any generative model at some point. Their slur and expletive filled outbursts make every critical response look bad by vague association.
Having sensible explanations like in TFA for the rules and criticism clearly visible should help. But looking at other similar patterns, I'm not optimistic. And education isn't likely to happen since we're way past any eternal september.
This is a good thing, it's an opportunity to make open source development processes robust to this kind of sabotage.
Yeah that seems to be their primary use case, if I'm honest. It's possible to use them ethically and responsibly, much in the same way it's possible to write one's own code, and more broadly, do one's own work. Most people however, especially in our current cultural moment and with the perverse incentives our systems have created, are not incentivized to be ethical or responsible: they are incentivized to produce the most code (or most writing, most emails, whatever), and get the widest exposure and attention, for the least effort.
Hence my position from the start: if you can't be bothered to create it, I'm not interested in consuming it.
For example, using AI as an editor. It doesn't write anything for you and you try and avoid suggestions unless you're stuck.
I was a food delivery driver back in the mid 00's to the mid teens. Early on, GPS was rare and expensive, so to do deliveries and do them effectively, you had to be able to read a map and mentally plan out efficient routes from the stochastic flow of orders coming out.
This acted as a natural filter, and "delivery driver" tended to be an interesting class of people, landing somewhere in the neighborhood of "lazy genius". Higher than average intelligence, lower than average motivation.
Then when smartphones exploded in the early 10's, the bar for delivering fell through the floor, and the job became swamped with people who would be best identified as "lazy unintelligent". Anyone who had a smartphone and not much life motivation was now looking to drive around delivering food for easy money.
Not saying the job was ever particularly glamorous, but it did have a natural mental barrier that tech tore down, and the result was exactly as one would predict. That being said, I'm not sure end users noticed much difference.
I have friends who order a lot of DoorDash and UberEats and they complain constantly about how awful the delivery service is.
The problem isn't that they haven't noticed, it's that they keep paying for the terrible service, even as the price goes up.
There are cool people on the other side as well, unfortunately those aren't usually who get assigned unless escalations take place.
Most shops are built based on juniors that need to build enough curriculum to go elsewhere as soon as they get some scars.
Yet not only those projects keep coming, now plenty managers dream about replacing those juniors with agents.
Don't get me wrong, tech is why I am here. But if it works, Alice and Bob don't care one bit about how the product exists.
well, they think they don't. until their pii gets leaked all over the internet because whoops our s3 bucket was publicly accessible, or until the service goes down because whoops our llm deleted the prod db...
Management, unsurprisingly deemed those precious. They could email them out anytime, working weekend to fix problems their kind were the cause. Sure sir.
They excel at communication. Perfecting the art.
Now LLMs are there to accelerate the trend.
I'd be more concerned if I was someone who signed up to play ping pong two hours a day and do a bi-weekly commit.
There was a time not so long ago where I was watching "a day in the life of a software engineer" videos on Youtube and I was wondering if some of these were parodies. I still remember one in particular which I'm pretty sure was a parody, but it was only marginally distinguishable from the others.
But submissions into slavery for immediate gain accomplishes little, and costs society a lot more (physical and mental health issues are a huge burden).
Those parodies you saw, they were caricature of elite engineers, who sacrificed decades of his life to become so competent. Can work from home, eat pasta while glancing over a PR and just hit approve.
That you resent the luxury doesn't make it undeserved privilege.
If you will forgive an appeal to authority:
The hard thing about building software is deciding what one wants to say, not saying it. No facilitation of expression can give more than marginal gains.
- Fred Brooks, 1986
True, regardless of that, for sure with LLM we are borrowing Technical debt like never before.
Do you work alone i presume? Everyone now is engineer. In my department, even managers are "writing code". Producing thousand of lines of ansible code, that nobody can review, with multiple lines of doc that nobody will read. It is just a mess.
IT isn't the only one - finance and law had the issue since forever, AFAIK - but now I'd rather be in a field that's _actively repellent_ to them.
Regarding quality overall, I agree, it's truly a cursed field. It was bad before; and with LLMs, going against that tide seems more difficult than ever.
This response 1000% was crafted with input from an LLM, or the user spends too much time reading output from llms.
Ray Kroc's genius was to make people forget that you get what you pay for.
Not picking on you in particular, but most of the anti-AI crowd can’t present their case compellingly and have an utter lack of humility.
Do You really want it?
There is also a second face of that: people are lazy. They wouldn't develop their own skills but rather they would off-load tasks to LLM-s, so their communicative abilities will be fade away.
That's looks like a strong dystopia for me.
How is this mutually exclusive with teaching better than most humans? Part of these "corporate" datasets include deep knowledge of the world's best literature and philosophy, for instance. Why can't it be both?
> Do You really want it?
If I'm in a hurry, don't know where to start, or don't have money for someone to teach me—sure.
> There is also a second face of that: people are lazy. They wouldn't develop their own skills but rather they would off-load tasks to LLM-s, so their communicative abilities will be fade away.
This is a recapitulation of the Luddite argument during the Industrial Revolution. And it's valid, but it has consequences for all technological change, not just this one. There was a world before Google, the Web, the Internet, personal computing, and computers. The same argument applies across the board, and the pre-AI / post-AI cutoff looks arbitrary.
Ah, so now we get to the "ed tech" question. What is teaching? Is there a human element to it, and if so, what is it? Or is it something completely inhuman? Or do we need to clarify what meaning of "teaching" we're talking about before we have a discussion?
Part of those datasets also include 4chan.
This wouldn’t be a plausible position.
That said, I think it depends how you use it. You can learn from explanations, and you'd better avoid "rewrite this for me and do nothing else" kind of approach.
> There are a lot of people who go through life by vibing. And honestly: that’s not automatically “bad.” Sometimes it’s even the only workable way to get through things. The issue is that “vibe-first” people tend to have a pretty loose relationship with truth, rigor, and being pinned down by specifics. They’ll confidently move forward on what sounds right instead of what they can verify.
I'll finish this post with a sentence containing an em-dash -- just to confuse people -- and by remarking on how sad I find it that people latch onto dashes and complete sentences as the signifiers of LLM use, instead of the inconsistent logic and general sloppiness that's the actual problem.
I think this is a very rosy view of programmers, not borne out by history. The people leading the vibe coding charge are programmers, rather than an external group.
I know it's popular to divide the world into the technically-literate and the credulous, but in this case the technical camp is also the one going all in.
I'm not saying that I'm no longer dealing with code at all though. The way I work is interactively with the LLM and pretty much tell it exactly what to do and how to do it. Sometimes all the way down to "don't copy the reference like that, grab a deep copy of the object instead". Just like with any other type of programming, the only way to achieve valuable and correct results is by knowing exactly what you want and express that exactly and without ambiguity.
But I no longer need to remember most of the syntax for the language I happen to work with at the moment, and can instead spend time thinking about the high level architecture. To make sure each involved component does one thing and one thing well, with its complexities hidden behind clear interfaces.
Engineers who refuse to, or can't, or won't utilize the benefits that LLMs bring will be left behind. It's just the way it is. I'm already seeing it happening.
But it absolutely has to be combined with verification/testing at the same speed as code production.
reminds me of the experience of reading a math text without doing the exercises, thinking that you've understood the material, and then falling flat on your face when you attempt to apply your "understanding" to a novel problem. there's a significant difference between passively reading something and really putting active effort into it. only the latter leads to actual understanding ime
On the other hand, LLM-generated code comments better than I do, so given a long enough time horizon, it could be more understandable at a later time than code I've written myself (we've all had the experience of forgetting how things work).
> On the other hand, LLM-generated code comments better than I do, so given a long enough time horizon, it could be more understandable at a later time than code I've written myself (we've all had the experience of forgetting how things work).
[1] https://en.wikipedia.org/wiki/Spaced_repetition
You ask questions about code when you implement something and if you cannot answer these questions, you go to code to find answers out and refresh your understanding of it.
For this to work you have to be interested in the understanding of the code and code should be created at the pace you can keep up.
Software engineers usually do create code economically because they need to remember and understand it. Vibe coders do not have this particular constraint, they just do not aim for most understandable code possible. Even if there are more comments in code.
However, this comes at the cost of losing track of the minute details of the implementation because you didn't write it yourself. I find it a bit analogous to code I've reviewed vs code I've written.
However, I've found using AI for code structure summary and questioning tends to be a good way to get around it. I might forget faster, but I also pick it up faster.
Then, after it says, yes I'm sure this is production ready and we're good to move on, you have Codex and Gemini both review it one last time, and ask it to address their feedback if it's valuable or not.
After all this, it's the only time I'll look at the code and review it and make sure it's coherent.
Until then, I assume it's garbage.
I'd estimate this still improves velocity by 10x, and more importantly, allows me to operate at a pace I couldn't without burning out.
You're just getting less work done on a slower cadence and asking the questions in design review and in code reviews...
i don't mind managing people, but i don't want to manage machines unless i can control them with the precise languages that the commandline and programming languages use. prompting a LLM is to vague an interface for me, the outcome is to unreliable, to unpredictable.
Any examples how you see some engineers being left behind?
I don't know where you live, but around where I live in Denmark you'd fail for not using AI at a senior interview in a lot of places. Even places which aren't exactly AI fans use AI to some extend.
The biggest challenge we face right now is figuring out how you create developers who have enough experience to know how to use the AI tools in a critical manner. Especially because you're typically given agents for various taks, which are already configured to know how we want things to be written.
Additionally there are the AI targets set by C suites based on what everyone is saying on TV, and what we can actually deliver based on the available data sets, integration points, and naturally those sign offs for data governance, and hallucinations guardrails.
If you can’t interview without immediately reaching for an LLM you are considered unfit to work here.
> If you don't jump off the cliff you're falling behind
It is anecdotal for sure, but it's a pattern that seems to be emerging around me that expectations of velocity increases, and those who don't use AI can't keep up.
Kind of.... I don't know. To get placed such requirements from the top down and not fight back, just take it head on, not even maliciously, don't even oppose it on a technical basis, just be like "yeah, you've now gotta ship faster or you're left behind, so therefore LLMs must be the future!", no critical thought attached. Is this shit coming from experienced engineers?
Preposterous we're relying on "it's better because I feel like", "dudes who don't use it are falling behind at work", "they ask for it in job interviews".
It would be an honor to be “left behind” by people who practice their craft with such carelessness.
(Frankly, I should probably stop replying to self-professed LLM boosters entirely since there’s a good chance I’m just chatting with an LLM.)
FWIW, I've opened a half dozen PRs from LLMs and had them approved. I have some prompts I use to make them very difficult to tell they are AI.
However if it is a big anti-llm project I just fork and have agents rebase my changes.
> utterly stupid
That's completely uncalled for.
EDIT: What exactly do you mean by:
> most likely reviewed.
Let's say every line was actually reviewed. That's still nowhere near good enough. The changes are being reviewed by the wrong people. Not the maintainers of the project, just some random folks who have inherited a vibecoded fork.
- Hooks (although there's no clean way to enforce they be "installed" on a clone), GHA Workflows (or their equivalents on other forges).
This might be my bias showing, but these are items I would consider table-stakes for a project of a certain size / level of popularity.
It feels like a lot of the "AI is shit at contributing" problems could be addressed in part by better automated checks and balances.
I do believe, however, that it would have a meaningful impact on the "drive-by" PRs that keep being used as examples; the thoughtless, throw-spaghetti-at-the-wall PRs that do not have malignant intent behind them.
Many large OSS projects would have the resources to eat that cost with Donors, Sponsors, and OSS hand-outs. That's why I clarified in my original post because I know this is not a general solution.
To large complex projects often the kernel of an idea is the core value of a contribution, and it can take a lot of iteration to figure out how to structure it. Token bashing until CI is green does nothing to ensure the best approach is selected.
Worst of both worlds with this, if you're doing it in a github workflow. You wind up effectively paying for the testing/validation layer of someone else's irresponsible LLM use.
I could have been more explicit on that nuance, I suppose.
Git supports pre-receive hooks. But big multitenant forges like GitHub.com don't allow you to configure them because they're difficult to secure well. (Some of their commercial features are likely based on them, though.)
If you self-host a forge, though, you can configure arbitrary pre-receive hooks for it in order to do things like prevent pushes from succeeding if they contain verifiably working secrets, for example. You could extend that to do whatever you want (at your own risk).
And the truth is, too, that it's super easy for an LLM agent to run a build and tests. Good faith contributors using LLMs will never open PRs that don't build not because they're willing to "go the extra mile" and do manual work, but because they give the slightest fuck and have any respect or consideration for the humans they're working with.
LLM spam presents a different problem than any of that stuff was meant to solve. It's a malicious act, and you're right that tooling that burns the defender's compute can't be a solution. :-\
It is quite strange that a large project like Zig would not have such a thing. I'm sure it's not trivial but it seems important to invest time into.
In my mind the commit (let alone the push to a publicly accesible server) should be done after, and only if, the automated tests are successfully executed. And there's no easy way to implement this, other than having a dirty branch that you discard after rebasing onto a more long lived one.
https://codeberg.org/ziglang/zig/src/branch/master/.forgejo/...
It's just bad/wrong/context lacking decisions and mental models it introduces, that if not carefull will just create a massive mess of a codebase. (I know, because I've tried, and had to deal with it)
And if someone vibecodes a PR and it works, why dont they just share the prompt so a repo owner could vibecode it themselves?
And in my experience it's quite hard to figure that out by quickly looking at it.
Not to mention that contributions on github (almost?) never include the prompt chain anyway, so the status quo is even worse
Imagine there's no AI, but for some reason you have people hiring armies of cheap overseas devs and using them to produce mediocre quality drive-by PRs. The effect would be the same.
AI can be used to make quality code, but that requires careful use of the tool... like any other tool. This isn't careful contributions made by someone who knows the project and its goals and is good at using the tool. This is spam.
To reject submissions where the dev "consulted ai" is like rejecting iron ore that was mined by a machine rather than a human. The quality of the ore is what should be measured, not how it was obtained.
The discourse around AI in the arts, and other creative and craft fields, is utterly identical to the discourse around photography when it came out to the point that you could search and replace terms and have the same dialogue.
For some personal projects I still stick to the basics and write everything by hand though. It’s kinda nice and grounding; and almost feels like a detox.
For any new software engineer, I’m a strong advocate of zero LLM use (except maybe as a stack overflow alternative) for your first few months.
I would love to see a model trained to behave way more like a tool instead of auto-completing from Reddit language patterns…
> Parallel semantic analysis has been an explicitly planned feature of the Zig compiler for a long time, and it has heavily influenced the design of the self-hosted Zig compiler. However, implementing this feature correctly has implications not only for the compiler implementation, but for the Zig language itself! Therefore, to implement this feature without an avalanche of bugs and inconsistencies, we need to make language changes.
Bun's fork will exhibit indeterministic behavior.
3000 line LLM commit is not that.
I feel like if their goal is to prioritize contributors over contributions, it'd also logically follow that they should try to have descriptions where possible? Just to make exploring any set of changes and learning easier? Looked it over briefly, no Markdown or similar doc changes there either.
I mean the changes can be amazing, it's just that adding some description of what they are in more detail, alongside the considerations during development, for new folks or anyone wanting to learn from good code would also be due diligence.
edit Okay, I set the bar too high here with "best human developer" and vague "good AI processes". My bad. Yes, LLM is not quite there yet.
We're already at the point talking about best vs. best.
We definitely are not close to that point though and it's unclear if/when we will get there.
If I do the latter and submit a PR to something like Zig, I'll be certainly caught doing it and rightfully chastised. If I do the former, my PR will be better without anybody besides myself having any way of knowing how it got better. Probably I do something in between when I contribute to open-source these days.
Blanket banning all of these seems like a bad idea to me. It actively gates people like myself from contributing, because I respect these people and projects that much. It feels like I would be doing something they find disgusting if my work has touched an LLM and I obviously don't want to do that to people I respect. But it's fine, there are plenty of things to do in the world even when some doors are closed.
I do not presume to have any say on Zig project's well argued decisions[0] -- I'm not really even their user let alone someone important like a contributor. Their point of preferring human contact is superb, frankly. Probably a different kind of problem in an open-source project staffed with a lot of remote working people, where human contact is scarce.
in my projects i will reject any contribution that i do not understand. even if the contribution is handwritten by an expert developer. that developer will have to earn my trust like anyone else, like you would have too.
LLM contributions are non-deterministic, which means they can never be trusted.
therefore, if you use LLM to contribute, you can not earn my trust. if you believe that you can not create a meaningful contribution without the use of LLM then you are realizing that you are not skilled enough to understand the code that you contribute. because if you could understand it, then you could write it yourself. i want your personal contributions, not those of your LLM. i want contributions that the submitter actually understands. i want you to earn my trust by showing me that you understand what you are doing. i want you to grow your understanding of my project. none of this happens when you use LLMs.
if you are unable to make a contribution without the help of an LLM then you are not ready to contribute. try looking for smaller issues that you can work on instead until you learned enough to make larger contributions.
Fair.
> that developer will have to earn my trust like anyone else
What does it take to "earn your trust"?
> LLM contributions are non-deterministic, which means they can never be trusted.
Provably incorrect. LLM contributions can be reviewed, tested, and understood like any other contribution. There's nothing "special" about LLM contributions.
Contributions authored by human brains are also non-deterministic, perhaps if the author was feeling in a slightly different way they'd have formatted the code a bit differently.
> therefore, if you use LLM to contribute, you can not earn my trust.
The premise is wrong.
> if you believe that you can not create a meaningful contribution without the use of LLM then you are realizing that you are not skilled enough to understand the code that you contribute
What if I believe I can do so without an LLM, but that it could be even better with an LLM?
What if I'm great at understanding code, but terrible at writing it?
Again, this is a premise that you just decided to take as truth, without proof.
> because if you could understand it, then you could write it yourself.
False. I can understand a novel algorithm by reading and studying it, but perhaps I could have not come up with it myself.
> i want you to earn my trust by showing me that you understand what you are doing
I can easily do that even if my contribution involves LLM assistance.
> i want you to grow your understanding of my project
Ditto.
> none of this happens when you use LLMs
False. Why do you think so?
> if you are unable to make a contribution without the help of an LLM then you are not ready to contribute.
Again, this is your opinion and you have no way of proving it. I can prove the opposite.
multiple successful contributions of increasing complexity, among other things.
>> LLM contributions are non-deterministic, which means they can never be trusted.
> Provably incorrect. LLM contributions can be reviewed, tested, and understood like any other contribution. There's nothing "special" about LLM contributions.
read this comment to see what i mean: https://news.ycombinator.com/item?id=47968180
> Contributions authored by human brains are also non-deterministic, perhaps if the author was feeling in a slightly different way they'd have formatted the code a bit differently.
i can tell a human to focus on a certain issue. they will either listen and follow my instructions, or i will reject their contribution. the LLM is almost guaranteed to not follow all my instructions and make changes i didn;t ask for. see my comment above.
>> therefore, if you use LLM to contribute, you can not earn my trust.
> The premise is wrong.
how so?
>> if you believe that you can not create a meaningful contribution without the use of LLM then you are realizing that you are not skilled enough to understand the code that you contribute
> What if I believe I can do so without an LLM, but that it could be even better with an LLM?
what you believe is not relevant. only what you can convince me of. you'll have to first show that you actually can work without an LLM before i will consider your contribution.
> What if I'm great at understanding code, but terrible at writing it?
your problem not mine. if you are terrible at writing code but good at understanding it then it's your choice to only do code reviews. you can still make a meaningful contribution that way. i'd even let you write code so you can practice that, but i am not interested in your LLM generated code.
> Again, this is a premise that you just decided to take as truth, without proof.
i don't need proof. i need trust. you need to convince me that your code can be trusted.
>> because if you could understand it, then you could write it yourself.
> False. I can understand a novel algorithm by reading and studying it, but perhaps I could have not come up with it myself.
that's called learning. once you learned it, you can write it. but in order to effectively learn you also have to practice. if you let LLM write all your code then you are not practicing, so you won't improve.
>> i want you to earn my trust by showing me that you understand what you are doing
> I can easily do that even if my contribution involves LLM assistance.
it depends on the level of assistance. i am not ruling out use of AI to do research and learn, just don't let it write the code for you.
>> i want you to grow your understanding of my project
>> none of this happens when you use LLMs
> False. Why do you think so?
as i said above, if you don't practice writing the code yourself you are not learning. not enough at least to satisfy my expectations.
>> if you are unable to make a contribution without the help of an LLM then you are not ready to contribute.
> Again, this is your opinion and you have no way of proving it. I can prove the opposite.
whether you are ready to contribute to my project or not is not something i need to prove. it is a choice based on my preference which depends on the amount of trust you have earned. you can not prove to me that you are ready to contribute. this is not a standardized test that if you pass you automatically qualify. you can only convince me by earning my trust. this is a human decision, based on feelings.
I accept most things you said there as valid opinions, but this is where the logic goes wrong.
I use LLMs to give me more from the only resource (now that my basic and mid-level needs are largely met) that ultimately matters: time. That means that I need to waste far less time in front of the computer, typing code, and use far more time doing more useful things, like hobbies, art, being with my children.
But as I said before, every project is obviously allowed to make their own rules, and contributors should obey those rules. There are plenty of projects that take both AI deniers and plenty of projects who prefer AI aficiandos.
At least for now. My belief is that one those groups will fade away like horseback riding did, but we'll see. Perhaps you have heard the famous stages quoted by many different people in different forms: first an idea is ridiculed, then it's attacked, then it's accepted. Some open-source communities have clearly entered the attacking phase in the last year so.
[*] in my opinion it takes more time to verify that the LLM code is correct than it takes to write it yourself. based on that, if you save time using an LLM then you didn't spend enough time to verify that the code is correct.
Some open-source communities have clearly entered the attacking phase in the last year so
i feel it's more like defense, but yes.
Billion times faster than a human, no tiring, no miscalculation, no brain-fart, no cheating.
> So while one could in theory be a valid contributor that makes use of LLMs, from the perspective of contributor poker it’s simply irrational for us to bet on LLM users while there’s a huge pool of other contributors that don’t present this risk factor.
> The people who remarked on how it’s impossible to know if a contribution comes from an LLM or not have completely missed the point of this policy and are clearly unaware of contributor poker.
The point isn't about the 3000 line PR, it's about do we think the submitter is going to stick around.
Because the pro-group are whining that the policy is preventing the merge, when in actual fact even if the policy did not exist, the PR is crap anyway.
That may be the case, but the bun project only needs zig to correctly compile bun. The zig project needs to be able to correctly compile all existing and possible zig programs.
I haven't reviewed things, but it's possible and even likely (at least based on my own experience with LLMs) that the validation is mostly focused on bun compilation.
I recommend reading the explanation given by one of the Zig devs, as it's a very clear and solid one.
The PR is probably fine for bun’s purposes. That doesn’t make it a good PR for Zig’s purposes, and could very well paint Zig into a weird corner.
> It cannot be merged no matter how good it is, due to the strict no-LLM policy.
This is about meta-discourse. Of course it’s against the policy. That’s the point of discussing the PR: to get Zig to change the policy, or at least provide an exception in this case. Or to argue the opposite.
In this case it isn't the blocker - the fact that the dev took the time to read the PR in detail, comment on it, and provide reasons why it could not be merged makes it very clear to me that the policy wasn't the blocker.
If they were going to enforce the policy for this PR, they wouldn't have bothered to read it. The only reason to read it is to see if the policy is waived for this specific PR.
As the Zig maintainer so patiently explained, no amount of "polish" can fix the PR because it is misaligned to the correctness that they require.
IOW, that PR is so far off the reservation, unless it is completely rewritten, it won't be accepted.
Rewriting PRs with LLMs is cheap, but often the output is no better than the previous revision (fixing one issue only to cause another one is very common IME). And reviewing each revision of the PR is not cheap.
I've had good experiences with people submitting AI generated PRs who then actually take the time to understand what's going on and fix issues (either by hand or with a targeted LLM generated fix) that are brought up in review. But it's incredibly frustrating when you spend an hour reviewing something only to have someone throw your review comments directly back at the LLM and have it generate something new that requires another hour of review.
In this case it looks like the answer is "Yes"; the PR was not dismissed immediately, it was first examined in great detail!
Why would the maintainer expend effort on something that was going to be rejected anyway?
I don't understand this PoV - have you ever come across a policy in any environment that wasn't subject to case-by-case exceptions?
Even in highly regulated environments (banking/fintech, Insurance, Medical, etc), policies are subject to exceptions and exemptions, done on a case-by-case basis.
The notion, in this specific case, that "well they rejected it because of policy" is clearly nonsense and I don't understand why people are pushing this so hard when the explanation of why an exemption can't be made for this specific PR is public, accessible and, I feel, already public knowledge.
are you too stupid to understand the notion of a hypothetical? how did you get on hn in the first place?
You also don’t sound smart enough to be calling others stupid.
It's a bit like saying speed limits don't apply on private property, therefore you can't have any traffic rules on your private racetrack.
That’s not how copyright works. If you don’t own the code, you can’t release it under a license. The question of how much human editing is needed to establish copyright is a huge question right now.
A healthy contributor community is more important than mere code performance, quantity of features or lines of code, etc..
I'm not sure how to tie this all back to the zig story other than to point out the stated premise that zig is not short of PRs and so they can pre-select for no-LLM contributions. I think that is a good move for them and I get the "contributor poker" idea. But, the game changes when the premise breaks and the flow of newbies reduces to a trickle. At that point, if there are still active zig people who still want newbies, they may need to broaden their net. But if/when that happens, it may be too late to recover by opening to LLM-assisted contributions.
If an AI improves developer productivity so much, why would maintainers of an OSS project want unknown contributors to sit in between the maintainer and the LLM? They'd be typing these queries into Claude Code themselves. To quote my colleague:
> We do not need a middleman to talk to AI models. We are not bottlenecked by coding.
In any REAL, workload, with good processes, code review makes speed of code generated a moot point. You still move as fast as you can review the code, and no, I won't debate that you can rely on LLMs, a deterministic language predictor, to determine the correctness of code; in the context of the business, and technical implications.
It's... pretty clear in the original conversation.
Having someone else be the AI-middlemen, just introduces additional complexity and confusion.
Something like using the AI to get an initial bad version, make some tweaks to the prompt, make some manual fixes, ask the AI to fox something else, noticing some new related feature and asking the AI to add it, making some benchmarks and deciding to remove a small feature, or perhaps deciding between two similar implementations, add a few more manual fixes here and there, run the extended version of the automatic test and find a weird bug in the unusual setup, make a few fixes with the AI and manually. So after 20 hours of work, the final version has only 50 lines that have been rewriten like 5 times each. Now the mantainer can review only the final version in 1 hour or so.
This is very different to spending 5 minutes asking the AI to write a patch, that has 1000 lines that does not even compile and sending it to the maintainer without looking at it.
I suspect the people who claim that AI works by only giving it high-level instructions are mostly working on "mindless" projects where a developer in the weeds wouldn't need to think very much.
"It's so easy, I could have done that myself"
Well yeah, but you didn't.
You're not suggesting the only metric of productivity is lines of code are you? And that the only benefit of using LLMs is for generating code you're too lazy to type yourself?
> LLM assistance breaks that completely. It doesn't matter if the LLM helps you submit a perfect PR to Zig
That’s the best rational I’ve seen so far, and fully support Zig decision here. I really appreciate their long term vision for both the community and actual project. I don’t think LLMs have such a great place in more collaborative efforts to be honest. Though we will see how things evolve, but I do see that when getting AI generated PRs I basically have to redo it myself (using LLMs, ironically… something I’m really starting to feel conflicted about)
Ofc, the scattershot 10k changes PR touching 30% of all your code files can be auto rejected without even looking at it. Who cares who or what wrote it.
And a small focusses PR from a new contributer that needs clarification which the author can not provide, shelve it.
But a blanket no-ai policy? I hear echos of business execs refusing email and demanding in person visits to remote offices for any interaction (not imagined. I knew an IT admin back in the late 80's who even refused to answer the phone and email as he felt that was 'too easy' and 'cutting in line', yes, the pysical hallway queue of people needing simple things like a login, quota adaptation or a password reset)
The tool is not your problem. Your selectivity process was never designed for low barrier access to participation. I have full sympathy for that. But focus on the real problem, the process, not some (rightly or wrongly) perceived feature filter to avoid changing how this works.
Now if you say "my project, my rules" 100%. And I sympatize very much with being overwhelmed by nuissance on a thing you love and care for.
Just don't throw out the baby with the bathwater.
The same argument applies to open source itself. Why use someone's project when you can just have the robot write your own? It's especially true if the open source project was vibe coded. AI and technology in general makes personalization cheap and affordable. Whereas earlier you had to use something that was mass produced to be satisfactory for everyone, now you have the hope of getting something that's outstanding for just you. It also stimulates the labor economy, because you have lots of people everywhere reinventing open source projects with their LLMs.
I've been thinking about this a bunch recently, and I've realized that the thing I value most in software now isn't robust tests or thorough documentation - an LLM can spit those out in a few minutes. It's usage. I want to use software which other people have used before me. I want them to have encountered the bugs and sharp edges and sanded them down.
The sanding down you refer to is what generates those tests and documentation.
Are you suggesting that LLM's can't test for people who use screen readers? Keyboard only users? Slow network requests?
You're acting like the issues an app faces are so bespoke to the actual app itself (and have absolutely no relation to existing problems in this space) that an LLM couldn't possibly cover it. And it's just patently wrong.
If you disagree with that, I think the onus is on you to show me that an LLM could simulate the full context in which a user interfaces with software. That's a ridiculous claim.
Feel free to show literally any evidence for this claim.
lol And you made the claim, not me. The proof is on YOU.
The status quo is that this capability does not exist. Whoever makes a claim contradicting the status quo has the burden of proof. I can't prove a negative.
And even with your logic, I did not make the original claim, it was made by simon.
Your statement now also makes little sense. For any nontrivial software project, the usage patterns and interactions with other systems are complex enough that the code itself does not contain enough context to understand how it is used, or what the invariants are.
There may be very simple codebases where an LLM can actually give you "thorough documentation" or "robust tests", but those are rare.
Its not rare. I've built 2 dozen line-of-business apps in it last handful of years that were glorified CRUD apps. Every environment I've been in has had a mix of the 2.
And even then, that's at odds with your absolute above. On top of being in a field that changes daily.
I wasn't really going for an exact, formal statement, but I can give you a formal interpretation of what I said above, if you want to be pedantic.
In general, you can't expect an LLM to produce thorough documentation or robust tests for nontrivial software, because the use of those software (i.e. how their interfaces are expected to behave) contains assumptions from the context in which they are used, and that information will not be encoded in the source.
If the above was somehow ambiguous, this should be clear and uncontroversial.
That is in fact what I did and if you meant otherwise, then yes I agree that currently there are plenty of cases in which those tools fall short and will never replace a human.
I don't think it's feasible to fully simulate the full depth of actual usage, given that (especially in the case of screen readers and the like) there's a great deal of combinatorial depth and context to the problem. Which screen readers, on which operating systems, and which users thereof?
So it's just the fact that others have already gone through the motions before I did. That's it really. I suppose in commercial settings, this is even more true and perhaps extends to compliance.
I regularly do both when trying to use library, especially unfamiliar to me.
But even in that case, you're reading the documentation. Just through a nondeterministic, hallucinating search engine.
sooo uhh how do _you_ learn how to use a new library? just throw random shit at the wall until something sticks?
Can it if we stop defining "robust tests" as "a lot of test code lines" and "good documentation" as "lengthy documentation"?
I didn't use the word good.
It may be able to spit out text that purports to be that, in a few minutes. But for most software, an LLM will not be able to spit out robust tests - let alone useful documentation. (And documentation which just replicates the parameter names and types is thorough...ly useless.)
Lolz! I haven’t encountered “code that institutions had been keeping to themselves” that got even remotely close to OSS in quality.
But there's no way that Google is releasing a model trained on it. Way too high of a risk of IP leakage.
I have worked during several decades in many companies, located in many countries, in a few continents, from startups to some of the biggest companies in their fields. Therefore I have seen many proprietary programs.
On average, proprietary programs are not better than open-source programs, but usually worse, because they are reviewed by fewer people and because frequently the programmers who write them may be stressed by having to meet unrealistic timelines for the projects.
The proprietary programs have greater quantity, not quality, by being written by a greater number of programmers working full-time on them, while much work on open-source projects is done in spare time by people occupied with something else.
Many proprietary programs can do things which cannot be done by open-source programs, but only because of access to documentation that is kept secret in the hope of preventing competition.
While lawyers, and other people who do not understand how research and development is really done, put a lot of weight in the so-called "intellectual property" of a company, which they believe to be embodied in things like the source code of proprietary programs or the design files for some hardware, the reality is that I have nowhere seen anything of substantial value in this so-called IP. Everywhere, what was really valuable in the know-how of the company was not the final implementation that could be read in some source code, but the knowledge about the many other solutions that had been tried before and they worked worse or not at all. This knowledge was too frequently not written down in any documentation. Knowing which are the dead ends is a great productivity boost for an experienced team, because any recent graduate could list many alternative ways of solving a problem, but most of them would not be the right choice in certain specific circumstances.
There's also the fact that when you write open-source code, you're writing for a friendly audience. I've often found myself writing the code, letting it rest for a few hours, then rewriting it so that it is easier to read. Sometimes, the code gets substantially rewritten before I push.
There's no cooling period when you write code during your 9-5 job: it works, it has the required test coverage, ship it and move on to the next task.
The whole point of having a civilization is that most things in life can be made someone else's problem and you can focus on doing one thing well. If I'm a dentist or if I run a muffler shop, there are only so many hours in a day, so I'd probably rather pay a SaaS vendor than learn vibecoding and then be stuck supervising a weird, high-maintenance underling that may or may not build me the app with the features I need (and that I might not be able to articulate clearly). There are exceptions, but they're just that, exceptions. If a vendor is reasonable and makes a competent product, I'll gladly pay.
The same goes for open source... even if an LLM could reliably create a brand new operating system from scratch, would I really want it to? I don't want to maintain an OS. I don't want to be in charge of someone who maintains an OS. I don't necessarily trust myself to have a coherent vision for an OS in the first place!
The Zig project is certainly far beyond such capability.
As someone who recently started using OpenSCAD for a project I find this attitude quite irritating. You certainly did not "have to" use popular tools.
The OpenSCAD example is particularly illuminating because it's fussy and frustrating and clearly tuned towards a few specific maintainers; there's a ton of things I'd like changed. But I would never trust an LLM to do it! "Oh the output looks fine, cool" is not enough for a CAD program. "Oh, there are a lot of tests, cool" great, I have no idea what a thorough CAD test suite looks like. I would be a reckless idiot if I asked Claude to make me a custom SCAD program... unless I put in a counterproductive amount of work. So I'm fine with OpenSCAD.
I am also sincerely baffled as to how this stimulates the "labor economy." The most obvious objection is that Anthropic seems to be the only party here getting any form of economic benefit: the open-source maintainers are just plain screwed unless they compromise quality for productivity, and the LLM users are trading high-quality tooling built by people who understand the problem for shitty tooling built by a robot, in exchange for uncompensated labor. It only stimulates the "labor economy" in a Bizarro Keynesian sense, digging up glass bottles that someone forgot to put the money in.
I have seen at least 4 completely busted vibe-coded Rust SQLite clones in the last three months, happily used by people who think they don't need to worry their pretty little heads with routine matters like database design. It's a solved problem and Claude is on the case! In fact unlike those stooopid human SQLIte developers, Claude made it multithreaded! So fucking depressing.
You definitely need to have a strong sense of code design though. The AIs are not up to writing clean code at project scale on their own, yet.
Not trying to denigrate the work here, as such. But this certainly didn't convince me that using AI to replace OpenSCAD (or any other major open-source project) is a good idea. The LLMs still aren't even close to being able to pull it off.
I mean, to be fair, a one-user project is not ever going to be as bugfree as a tens-of-thousands-of-users project. That's just inherent and not an AI issue. If you judge AI projects by that standard, they'll always come up short. It's a sampling issue. An AI project that's gotten to a level where it competes with a traditional project will always be buggier and less feature complete and polished, because AIs speed up development. It will simply have seen far less, well, polish to get there.
Civilization isn't monotonic. People keep solving the same problems over and over again, telling the same stories with a different twist. For example in 1964 having a GUI work environment with a light pen as your mouse was a solved problem on IBM System/360. They had tools similar to CAD. So why don't we all just use that rather than make the same mistakes again. Each time a new way of doing things comes out, people get an opportunity to rewrite everything.
Maybe this will be a real problem in a couple years though.
And you indeed get a lot of wheel reinvention by LLMs because that is now cheap to do. So rather than using some obscure thing on Github (like my stuff), it's easier to just generate what you need. I've noticed this with my own choices in dependencies as well. I tend to just go with what the LLM suggests unless I have a very good reason not to.
Because it takes hours/months/years of accumulated design decisions to get a great open source project. Something an AI agent can only approximate the surface of, unless you’re ready to spend a lot of time on it
So centralizing that common work is a benefit of open-source just as much with LLMs as it was before.
Iff it is doable, then it would be worth considering it as alternative.
> It also stimulates the labor economy, because you have lots of people everywhere reinventing open source projects with their LLMs.
not sure what you mean by that
This is only a valid strategy if you either
a) understand the problem domain well enough to make a judgement call on what the LLM shits out.
or b) don't care about the correctness of the project.
Obviously, many software devs feel comfortable enough with CS problems to validate the LLM solution, but a flower shop owner does NOT know enough about accounting to vibe code a bookkeeping project, so for a shop owner an open source option - with many human contributors and actual production use elsewhere - would be a much better choice.
Because it is incredibly expensive to write a replacement for semi-complex software? Good luck asking frontier models to write a replacement for Zig, Docker, VSCode, etc.
It's not that the project maintainer can use a LLM to generate a PR, it's that they choose not to.
To relate it closer to your argument. As a someone involved in a project that does X, I would find little value in collaborating with the "author" of another project built with AI to do X. Where as a project doing X were the authors actually wrote, understand the code, and thus the problem space better would be extremely valuable peers.
That said, it still feels like they are unnecessarily hobbling their project. LLMs are tools and they can help you think, research, and code. You can overuse them, yes, but you should embrace them where they help.
not accepting bun's PR for other reasons is totally fine (sounds like it's a core change where more thinking needs to be done), but simply banning all LLM authored PRs is unnecessarily restrictive. Just focus on the quality of the work.
Maintainers should get to spend their time developing stuff, not just reviewing low effort PRs. The flood of LLM code is changing the balance for the worse for maintainers, and I can totally see why they’d just want to ban it.
if someone made the same gigantic mess of a PR without LLMs, it would still be rejected, because it is a gigantic mess of a PR.
the low effort part is the problem. what if i made a great, focused, readable PR but had claude write it out? what if i carefully checked and deliberated each line, just as if i had written it myself?
granted, in the real world, 99.9% of slop PRs are written by LLMs. so i thought "okay, reasonable, ban the thing that is most likely to cause problems."
but then how does the "no LLM translators!" rule fit into that view?
I think LLM dev needs to take a better spec driven approach. The vibing is getting to be annoying.
They’ll still use even smarter LLMs badly no doubt, but I’m thinking that maintainers of open source projects will be able to more effectively use LLMs to review potential PRs to weed out the truly bad ones quickly.
It’s obviously an imperfect rule, and maybe it’ll change over time. But I am just saying that I understand why open source maintainers are doing this.
There is just no possibility for them to review all the low effort AI slop being thrown their way. Yes, some of it is going to actually be very high quality, but you don’t know that until you review it, which is the whole issue.
Because getting an LLM to do it yourself still takes time and attention bandwidth and tokens.
Look at it this way. If a human has interpreted their LLM use so well that they can submit to zig and not get caught, then the LLM use is acceptable.
What they are doing in practice is filtering off all the submissions from lazy people who dont sit between the LLM and the PR.
If you cant be bothered to cover your tracks enough to make the LLM output into a good PR, thats no longer the maintainers problem.
In a decade all of these anti AI policies will go away as the costs go up, and LLMs become less detectable. In the mean time it seems very efficient.
Now in my org, people who have great understanding of concepts, deeper engineering understand have exponential productivity. People who dont or new in the workforce, juniors, are generating hell-ish code without understand as long as it runs they think the job is done. And this is where the problem is.
The llm creates an intellectual gap within the org and it just widens it as more and more it gets used. You might end up not trusting stuff within the org if code is generated by later.
One fantastic usecase for me just recently was writing up a concept for an authentication daemon. With codex this is like a conversation where I pick from the suggestions, cross reference them with normal web-search and decide on a final draft which I then discuss with colleagues.
This "conversational" planning with integrated web-search (aka plan mode) is insanely useful. Also reviewing already written code with AI is purely beneficial in my opinion.
In my opinion the main caveat of AI is, you eventually have to be smarter than then tool. So for example if Codex suggests I should use tech-stack X then I must research and fully understand why this is actually good and still have to compare to other solutions. I think this is where the problem lies, some people skip this step which leads to so so many problems, and that's fatal. You MUST be smarter than the AI after your conversation and fully understand and be able to critique what it said.
The weakness of AI is that it is really easy to fall into lazy habits.
Something about having to talk to a machine like it's a human makes me fall for treating it like a human. I want to treat it as a probability engine that collapses to an answer based on input, but that input explicitly needs to be one that has it collapse to something a reasonably knowledgeable person would respond with, which more-or-less means talking to it like it is that kind of person.
I feel like it activates the social part of my brain and then I stop working with it properly. I'm still building the habit, though, only recently started taking the LLMs seriously as a tool.
I can generally make it produce excellent well-tested code. Far better than I could do in the same time on my own. But it's a challenge to keep on top of knowledge about everything it made.
While I haven't codified it anywhere, the policy I would like is for issues and PR descriptions to have no LLMs - there is no reason to ban code completely though IMO. I would say that would be pro human-communication and a stance I would like a lot.
If they are, we wouldn't be having this conversation because they will be fully autonomous
People who blindly submits LLM generated code or do not cite its usage really need to stop doing it
In the past, OSS projects were self-selective because you needed to be able to make working code, and if you did, you probably also reasonably did the right things as you spent years learning this, and have some sort of reasoning behind your feature, need, etc.
Today, even if the LLM was perfect and could reason well, it still does the bidding of the prompter - and you no longer have self-selection. Heck, it'll be difficult for zig devs to decide what's actually made by an LLM or a human anyway, I'm sure there's already LLM generated code in there - but at least these [human submiters] still need to be reasonably good at code.
I wonder if we'll end up with "only human with trusted badge of honor" can commit, and/or "LLMs now reason well enough to tell you: 'no, f off, this feature, plan, idea is garbage I'm not generating it" hehe.
It's a completely unenforceable virtue signal.
They won't I suspect. If there isn't any good way to give them a good smack for doing it then I don't know what would make them stop.
The "smarter/better" attributes you are worried about LLMs not having happen between iterative steps, when the human is inspecting the current state of the software and compares it to the desired state of the software (in their mind's eye). The human then course corrects for the next iteration.
This would be like if Michelangelo carved the David using a robotic 6-axis chisel. It takes him 1 month instead of 3 years because he can convey his initial vision to the robot and then iteratively refine the granite until it matches his vision.
You can try to claim LLMs don't invent new things, but humans using LLMs absolutely invent new things (source: myself).
You rebutted with (paraphrasing) "no, you can't build compilers with LLMs because LLMs don't invent new things"
I used a lot of words to demonstrate that you can invent new things with LLMs, including compilers, as long as it's a human + LLM iterative loop and not an unsupervised LLM running in a vacuum.
LLMs are a powerful tool like we've never had before. You don't expect a chainsaw to cut down a tree by itself and carve the wood into a statue or a new compiler. LLMs aren't mind-reading autonomous creators, they're more like a mech suit that can increase your capabilities. They have flaws, but until something better comes along, it sure seems like they're the future.
That's a fair thing to ask, though it seems like people will arrive at very different conclusions there.
I don't even have an issue with AI generated code; it's a tool, and if it works you should use it. What bothers me is that we're getting millions of lines of AI generated code, that no one is reading, and I don't see the point; it feels like at this point we're doing the rookie thing of "committing the binary".
I think we would really need determinism to make this a reality [1], but ideally what I would like people to do is only commit the prompts and treat the emitted code similar to how Github releases works today: like a binary artifact. Write your tests by hand, make sure that the prompt always satisfies those tests (and for the love of god please learn property based testing so that you're not just emitting answers that satisfy the test) and then assume that the LLM will give you competent code.
[1] Though not completely! We're already committing code without fully reading it so I'm not convinced determinism completely matters.
For example I got a working application with minimal prompt like "I need an X11 tray icon app showing battery charge level". BTW result: https://github.com/baverman/battray/
Now I'm trying to implement a full taskbar to replace bmpanel2. Results are very positive. I've got feature parity app in 1h with solid zig code.
In order to even say this, you need to have knowledge and understanding about the language. I suspect you are not the intended target of this policy. They are defending their project with a harsh policy, knowing full well there are false negatives. Contributions for FOSS was already in borderline crisis mode before LLMs so it makes sense they’re desperate.
Their bet would be Venn diagram of LLM user overlaps with irresponsible. I think that’s correct, but not because good programmers suddenly become irresponsible when they use LLMs, but rather that an enormous barrage of bad programmers can participate in domains they otherwise wouldn’t even know where to begin.
- 2 are python resource hog
- 2 from AUR don't compile with modern GCC.
- 1 uses gtk battery icon, but uses dark version on dark taskbar, unreadable.
- 1 shows just black square.
Think of it like a robotic exoskeleton. It will be used to let people do bad things, and stupid things, but it will also be used to help people who otherwise couldn't do things do good things, or become more able than they were. For some people AI means being able to code where they couldn't before. For many it will mean learning to code by observing what the AI does. For others it might mean being able to code a lot faster, or even a lot better, than they already could. And yeah, for some it will mean they atrophy in some skills while they develop others. The exoskeleton will have the same problems, if anyone ever brings a decent one to market, but on the whole it will be an enabler.
I don't see how cultivating a contributor who's using an assistive technology is worse than cultivating a contributor who isn't. Apart from that it can be more challenging, of course.
perhaps that's what the maintainers should be doing after all. it still takes time and tokens, though; neither is free.
I'd personally rather have the maintainers spend the time writing as much docs and specs as possible so the future LLMs have strong guardrails. zig's policy will be completely outdated in a couple years, for better or worse. someone will take bun's fork, add a codegen improvement here, add a linker improvement there and suddenly you'll have a better, faster zig outside of zig.
Someone forking it and makeing it better with AI is a possibility. If that happens will know it was better for the project for the maintainers to just review the code. If that happens, they can probably become maintainers in the fork. Or maybe they don't like that work and could just go do something else
Let's take a look at some of them:
1. Project control – if a LARGE company implements thousands of lines created by LLMs day after day – who is ultimately responsible for the project's progress? "You accept hundreds of PRs, so why not this one?"
And one more thing: will you be able to change the code yourself, or will you be forced to use LLMs? What if one of the "AI companies" implements a strict policy preventing "other tools that XXX" from editing the codebase?
2. Ownership. If most of the code was taken by an external company from their LLM, what about ownership of the code? The authors of Zig, the company, the authors of the original code, stolen by LLMs?
3. Liability. In the near future, a court may rule that LLMs are unethical and should not recombine code without the owners' prior consent. Who is responsible for damages and for removing the "stolen" code? The owners of Zig, the company that creates pull requests, or the authors of LLM programs?
4a. Vision. Creating and maintaining a large code base is very difficult – because without a broad perspective, vision, and the ability to predict and shape the future – code can devolve into an ugly mess of ad hoc fixes. We see this repeatedly when developers conclude, "This is unsustainable; the current code base prevents us from implementing the correct way to do things."
LLM programs cannot meet these requirements.
4b. There's another aspect – programming languages particularly suffer from a lack of vision or discipline. There are many factors that must be planned with appropriate capacity, vision, and rigor: the language itself should be modeled in a way that doesn't prevent correct implementation of behaviors. The standard library must be fast, concise, and stable. The compiler itself must be able to create code quickly and repeatably.
Users hate changes in a language – so if a language changes frequently, it is met with harsh criticism. Users hate incompatibility. Users hate technical debt and forced compatibility. Yes, there are conflicting requirements. The author of Zig understood this perfectly, having already gone through it himself (see, for example, "I/O Redesign").
This balance, in all aspects, is the pillar of human creativity.
To be honest, I'm not a huge fan of Zig because I dislike the tight syntax: too many periods and curly braces, which is why I prefer Odin. But I have a lot of affection and respect for Zig and its authors.
I think this is a great policy by the Zig team.
I maintain my own private fork with some small modifications which I started polishing up this week to release it for a talk that I'm preparing.
The project I'm using this on is an ecommerce site [0] written in 100% Ur/Web with a hand-rolled backend ERP system written in PHP (not by me) which I am slowly replacing bits of with new Ur/Web code. As of today, we have 22223 lines of Ur/Web code, weighing in at 701 KiB.
This already happens to some degree on large software projects with corporate backing (Web engines, compilers, etc.), where it is often not trivial to start contributing as an independent individual.
Reasonable people can disagree on whether one approach is inherently better than the other, as ultimately they seem to be optimising for different goals.
If I have a test harness, and LLM workflow setup, it is easier to just write new code myself. I am not giving away my "secret sauce". And I will not have a debate "why this simple feature needs 1000 new tests...", and two days just to make a full release build.
For merge I have to do 99% of work anyway (analyze, autotest, build, smoke, regression test). I usually merge smaller commits just to be polite (and not to look like one man show), but there is no way to accept large refactoring!
…in theory. In reality, I’m sure a policy like this can’t be selective and fair at the same time. Pick one!
"What are the intellectual-property risks of using generative AI tools? The Oracle Contributor Agreement (OCA) requires that a contributor own the intellectual property rights in each contribution and be able to grant those rights to Oracle, without restriction. Most generative AI tools, however, are trained on copyrighted and licensed content, and their output can include content that infringes those copyrights and licenses, so contributing such content would violate the OCA. Whether a user of a generative AI tool has IP rights in content generated by the tool is the subject of active litigation."
It's also why model collapse is not a thing despite everyone wanting it to be.
That's why the code you get from the post-November models is so much better than older models.
In the Doom engine, for example, he has hard coded lots of things directly in the C engine code that really should be part of the regular game code.
The fact is, LLMs are incapable of invention and synthesizing new ideas. They can't contribute to the zig compiler because they have not been trained on the zig compiler, because it doesn't exist yet.
Yes, they can churn out simple apps, and quickly. That's a pretty useful thing, especially for people that don't know how to write code. But that's not as revolutionary as you think it is.
Others have mentioned the hype around 3D printing several years back. Kinda the same story. People thought manufacturing was dead, stores were dead. You'd just print everything you need yourself! Turns out it's not quite like that. It could still get to that point someday but these are hard problems that take time.
Similar with LLMs. It took us, what, 70 years of computer and AI research to get to this point? And people assume we're going to skyrocket way past this point in another year or two?
Is the barrier to entry really that you must be able to perfectly recreate the project from scratch before you can possibly have anything to contribute?
I don't think it's fully appreciated how much of the hard work of "synthesizing a new idea" is just combining existing ideas. LLMs have given me brand new algorithmic ideas with precious little in the way of a spark on my end to make that happen, and not just a few times either.
Mind you, that workflow is arduous and involves a huge amount of experimentation, screening through interesting but ultimately wrong ideas, screening through outright bad ideas the LLM can't help but spew out as well, and manually massaging the results into something useful. It exists though.
Once you introduce constraints (cost, limited context), the system starts behaving very differently — more like an economy than a pipeline.
I don't think it's "poor character", though, so much as "willing to develop the deep mental model required for effective contribution".
Founders go 1000x your own projects and leave real programmers alone.
When you have junior people come in with PRs and you do the whole hand-holding thing so they learn and grow and all that, they're there because my project is famous, they want to get credit (which I give them), then they're off to get jobs whereever and they are working with completely different technologies, and you never hear from them again either, because, of course, they're now busy!
Really, outside of my core group of hangers-on, Claude is the only contributor we have that doesn't leave us.
> This makes a lot of sense to me. It relates to an idea I've seen circulating elsewhere: if a PR was mostly written by an LLM, why should a project maintainer spend time reviewing and discussing that PR as opposed to firing up their own LLM to solve the same problem?
well yeah. I almost use PRs now just as a lazy means of issue prioritization. I'd love if github had more fine-grained controls to disable PRs but allow occasional contributors in (they don't).
https://ziggit.dev/t/bun-s-zig-fork-got-4x-faster-compilatio...
>There’s the 4x speedup claimed by the Bun team, already available on Zig 0.16.0!
>Each [incremental] update is taking less than 0.4s, compared to the 120+ seconds taken to rebuild with LLVM. In other words, incremental updates are over 300 times faster on this codebase than fresh LLVM builds are. In comparison, an enhancement capped at a 4x improvement is pretty abysmal. [..] Again, this feature is available in Zig 0.16.0—you can use it!
My blog is a combination of different content types. "Entries" are the ones I spend the most time on - https://simonwillison.net/entries/
Links and notes are more short form - I try to keep the quality high (especially with regards to accuracy) but they're also much higher volume than entries: https://simonwillison.net/blogmarks/ and https://simonwillison.net/notes/
What were you trying to imply by "very convenient"?
If you use the tool to yeah, go one shot a ton of garbage then it will in fact be garbage.
For those who are pissed because a large OSS project isn't accepting LLM generated slop: Fuck off!
However, I wanted to give Zig a try in an agentic coding scenario. For tasks that would take a few seconds when choosing Python, Java, or JavaScript as a target language, it would take tens of minutes and waste millions of tokens before producing anything.
Almost any model gets stuck trying to figure out the correct syntax and correct libraries for a specific Zig version, fighting with compiling and figuring out function call parameters, frequently taking it wrong and going on side quests for things that should just work.
I guess the relative lack of resources and the language instability don't play well for models that try to generate Zig code. Using specific tools like zig-mcp helps only a bit.
Until LLM support for Zig improves (one needs to spend significant resources for that to happen), LLM-generated Zig code won't be good enough for either Zig programmers or Zig contributors.
(Ok ok I think we lost the fight already. I see soooooo many people using AI tools on github in the last ~2 weeks alone, claude in particular literally infiltrated everything there.)
I was also blocked from the Zig github repository, after being a frequent contributor to issue discussions, for reasons unknown (I was never informed, I just found out when I could no longer put a thumbs up on a comment).
You may as well say "if someone else can do it I'll just do it myself". It takes skill and taste to know what to ask, wisdom to recognize mistakes, and time and money to fix them.
That's exactly the sketchy part here. They turned down known, working and tested, code that came from a partner (bun) due to this policy. Code that 4x'd compile speed.
A general ban makes sense based on their rationalization ("contributor poker"[0]). A total and inflexible ban can lead to a worse outcome for everyone though.
If a senior, experienced, contributor vouches for the code it shouldn't matter if they hand crafted it on stone tablets, generated it with yarrow sticks, or used gpt-3.
No; they turned it down because the vibe-coded PR was crap.
> The rewritten type resolution semantics were designed to avoid these issues, but Bun’s Zig fork does not incorporate the changes (and has not otherwise solved the design problems), which means their parallelized semantic analysis implementation will exhibit non-deterministic behavior. That’s pretty much a non-starter for most serious developers: you don’t want your compilation to randomly fail with a nonsense error 30% of the time.
The flip side of that is that if such a contributor vouches for code that turns out to be poor-quality, this should severely damage their reputation. I've found far too many "senior" developers will give AI a pass on poor coding practices.
> Put more simply, we are going to make these enhancements, but hacking them in for a flashy headline isn’t a good outcome for our users. Instead we’re approaching the problem with the care it deserves, so that when we ultimately ship it, we don’t cause regressions.
These exact changes are already on the roadmap and Bun’s PR is rushing ahead.
I mostly agree with the assessment.
IMHO: hard, inflexible rules like these are always deeply rooted in biases and personal convictions, not in facts. The suggested policy amendment by Claude at the end is much more honest, logical, and palatable.
https://claude.ai/share/abb3e667-252a-4b34-86f7-a064ba260d2a
This reminds me of something funny I noticed about AI. Let's say you ask it what it thinks of an email you just drafted. It will provide corrections.
Delete that session, and ask it about the corrected email. It will provide more corrections.
Repeat. It always provides more corrections. Sometimes returning the recommended email back to a previous state.
This is basically what's gonna happen when people argue-from-AI. It's the same cycle but because control is distributed the individuals participating can't see how stupidly pointless it is.
No, I don't think that was the argument. As I understood it, unassisted contributions have higher chances to grow a trusted contributor. Not 100% vs 0% chances, but statistically higher. So, given limited resources, it makes sense to prefer unassisted over assisted contributions.
Why would a contributor that uses AI assistance have fewer chances to be trusted?
I'm not talking about AI slop, but a contributor that takes time to understand a problem, find a solution, and discuss pros/cons alternatives. Using LLM assistance, of course.
please read my explanation here:
The more I think about it, the more nonsensical it is. - What if I do everything by hand, but have an LLM review my work at the very end? - What if I have an LLM guide me through the codebase just by specifying the files I should read and in what order, but I do all the reading myself? - What if I do everything by hand, but then use an LLM to optimize a small part of an algorithm?
You can easily see how absurd it is to completely ban LLMs.
What matters is the quality and correctness of the contribution. Even with heavy LLM usage, unless the developer understands what problem they're solving, the quality will be sub-par.
unlike LLMs, those are deterministic. the IDE doesn't even change the code. auto-completion only has a problem if it is done with AI.
Why is auto-completion a problem if it's done with AI?
anything done with AI is a problem because it is essentially unpredictable. auto-complete is on the fence because you presumably are still able to pay attention that it completes what you want but it depends on how diligent you are when working and how much i trust your diligence.
Even then, you can clearly see that the LLM will try its best to follow the instructions. The result might not be 100% predictable, but it is somewhat predictable depending on the task.
After the LLM does what it has been asked, you can review it, iterate on it, test it, and so on. And if you're trying to make anything worthwhile, you will do so.
Lack of determinism is not a practical concern.
Lack of determinism is not a practical concern.
it is to me. it's a knockout criteria. it is the only reason that keeps me from using LLMs for coding. nothing else is as serious an issue to me as this.
here is why: i tell the LLM to build something with requirements A B C D and E. it builds, i review and i find A B and D are good, C and E are broken. i tell it to fix them, it does, so C and E are fixed, but now A is broken. i tell it to fix that, and i have to keep iterating until i find a combination where everything works. in every iteration any part can randomly break, so for every iteration i get changes all over the place. they never are confined to the issue i pointed out. i have to review the whole thing every time. that's what i mean by lack of determinism, and that is a serious practical concern because instead of getting done in two or three iterations it requires dozens of them. see my related replies elsewhere. i just don't want to work that way.
That's not any different from LLM-assisted writing -- humans are inherently non-deterministic as well :)
The other fallacy is assuming that everyone else's experience with LLM-assisted writing is the same as yours. Personally, I've rarely encountered the issue you've mentioned -- most of my LLM-assisted coding has been a net positive and quite straightforward.
Perhaps it's the nature of the problem I'm working on, perhaps it's the model I chose, perhaps it's my prompting skills. It doesn't matter -- you just cannot assume that because something doesn't work for you it doesn't work for anyone else.
The other fallacy is considering LLM-assisted coding a binary option, like the nonsensical Zig policy does.
I agree with you that "vibe coding" something from scratch will likely result in poor quality and many iterations. But that's not the only way to use LLMs.
You can ask LLMs to review hand-written code. You can ask LLMs to optimize a specific part of code. You can ask LLMs to apply a specific refactor. You can ask LLMs to brainstorm solutions to a problem. You can ask LLMs to autocomplete patterns.
I could go on. This stuff works. It is helpful.
Assuming that everyone who uses LLMs is incompetent and preventing them from contributing because of a hunch or your own negative experiences is just asinine.
that's irrelevant. my choice can only be based on my experience. i am unable to verify your experience, because i am not you. we have different tolerances, and if it works for your project, then fine.
you just cannot assume that because something doesn't work for you it doesn't work for anyone else.
we are talking about contributions to my project. if LLM coding doesn't work for me, then your LLM created contributions won't work for me either because i won't trust them. you can't legislate or enforce trust. trust can only be earned. lack of trust means i have to spend more effort to verify your code.
edit: Can't reply because I've posted a whole 4 times.
I believe we have different world views which is hardly a disagreement. Answering my question could pretty well highlight our difference of opinion.
I’m not saying whether or not that’s good or bad. I agree with their approach, but that’s just my opinion and who am I to say what’s right or wrong? I think there’s value to LLMs as a tool to search and learn, but I’m also worried that LLMs make it really easy to focus on only the result and not the process. That process can be really valuable in building good teams, while LLMs can be really good at churning out an assembly line of code.
But it seems that the Zig policy implies that. Otherwise what would be wrong with interacting with contributors using LLMs?
Lmao bro has completely outsourced their thinking to AI, this is comical
Now read the actual points made by the AI with an open mind and a critical mindset, instead of dismissing them because they were not written by a human being.
The point I'm making is that this policy is so stupid that even an LLM can easily figure out the logical flaws. Perhaps an LLM could have also helped you figure out the point of my original comment.
How does this have anything to do with ethics? Its their project not yours, they can reject your PR for whatever reason, including you using LLMs for developing that PR. Also they're not assuming autonomous agents submitting PRs. They're saying that they do not accept PRs where any part of the thinking process was outsourced to a LLM.
Even if you disagree with their opinion, the ethical thing to do is to not interact and move on. Not to try to sneak in your LLM assisted PRs without the maintainers consent.
My fraternity's national organization refused to take photos over email for the newsletter because they got a virus.
It's a short-sighted policy that's akin to "throwing the baby out with the bathwater."
It's a critique of low effort PRs compared to the high effort review they require.
There are plenty of less stringent projects for people who to get better at open source to contribute to.
“Artisanal” and “Zig” are just about synonymous
Doing manual reviews of everything is very labor intensive and not scalable. However, AIs are pretty good at doing code reviews and verifying adherence to guard rails, contributor guidelines, and other rules. It's not perfect, but it's an underused tool. Both by reviewers and contributors. If your contribution obviously doesn't comply with the guidelines, it should be rejected automatically. The word "obviously" here translates into "easy to detect with some AI system".
Projects should be using a lot of scrutiny for contributions by new contributors. And most of that scrutiny should be automated. They should reserve their attention for things that make it past automated checks for contribution quality, contributor reputability, adherence to whatever rules are in place, etc. Reputability is a good way to ensure that contributions from reputable sources get priority. If your reputation is not great, you should expect more scrutiny and a lower priority.
Until the contributions are cheap and correct, you need valuable contributors more than you need the contributions.
You point would be valid when we get to a point of contributions all being both correct and cheap. Right now they are only cheap.
It takes like 5 minutes to spot garbage PRs manually. LLM can flood you with a wall of text where only half of the stuff make sense. Also, they can't really spot bad architecture. It's a compiler in an unpopular language, don't forget that.
The real bottle neck when you want to grow is connecting with the right people. An LLM is not helping with that if you want to build a community. When you use LLM to skip the need to understand a problem how are you ever going to get a reputation that I can trust?
The post is not about reputation it about seeing how people respond and work with you in a community.
EDIT: I see that you frame it as a help and a tool and sure it might work, but I feel like it is just another obstacle.
I suggest we also automate the distribution and the use of software with AI as well, and then just all go to the beach and sip on some cocktails or something.
Or in other words: Good luck with that.
Without AI, I’m a guy spending years learning C++ in spare time I don’t have to develop software concepts and solutions I want to work on TODAY.
The ZIG project, to me, has a place. Legacy coders right now do need protecting.
It’s not people like me that they need protection from.
It’s not even language models they need protection from.
What they need protection from are the corporate structures who falsely believe that this technology makes them obsolete.
The article talks about “playing the person, not the cards” and that thinking has one fatal flaw: the vibe coder is a person. The vibe coder may have creative agency that the legacy coder does not.
Look, I still cross up French and Spanish words because I took a year of each, C++ syntax, Python syntax, HTML, I understand their structures but I’m liable to start out writing a Python script and wind up with half a web page and a brutal error message in my IDE environment.
Zig’s motivation is correct in many ways I think. I am not really their target audience or their target coder. But I am also not their target enemy. Put the right group of legacy thinkers in my think tank, and the code would get even better.
-The Court Jester of Vibe Code